March 22, 2024ICT ExpressOpen Access

A study of the relationship of malware detection mechanisms using Artificial Intelligence

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Evolving security threats hinder standard evaluations of malware detection mechanisms, leaving cross-study performance comparisons largely fragmented.
Survey synthesis categorizes shallow learning and deep learning architectures into an organized classification table to map current defensive mechanisms.
Highlights established criteria across artificial intelligence models, allowing researchers to contextualize current workflows and orient future malware studies.

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Implication

Survey demonstrates criteria for malware detection mechanisms across artificial intelligence architectures, highlighting benchmark strategies to guide future research directions.

Cite This Study

Song et al. (2024) studied this question.

synapsesocial.com/papers/68e72b9db6db6435876a578c https://doi.org/10.1016/j.icte.2024.03.005

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Implication

Survey demonstrates criteria for malware detection mechanisms across artificial intelligence architectures, highlighting benchmark strategies to guide future research directions.

A study of the relationship of malware detection mechanisms using Artificial Intelligence

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Discussion

Implication

Cite This Study

A study of the relationship of malware detection mechanisms using Artificial Intelligence

Key Points

Discussion

Implication

Cite This Study

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